Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Abstract

1Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO 2 ) and particulate matter (PM 2.5 ) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO 2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM 2.5 showed little spatial variation; therefore, daily PM 2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV 1 ) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO 2 and PM 2.5 . These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily householdlevel NO 2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies -in particular, for research focused on short-term exposure and health effects.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment

“…While seasonal models are of interest to understand how particular forms of the built environment interact with meteorological and climatological factors to change the spatial distribution of air pollutants throughout the year, the ultimate contribution of air pollution models is to improve exposure assessment for epidemiological studies. To this end, Johnson et al (2013) demonstrated that temporal refinement of a NO 2 LUR model with regulatory monitoring data can improve exposure estimation for research on short-term effects on health.…”

Air pollution and the incidence of ischaemic and haemorrhagic stroke in the South London Stroke Register: a case–cross-over analysis

“…1 In terms of same day exposures (lag 0), this meta-analysis found small positive associations between the risk of hospitalisation or mortality for stroke and each of PM 2.5 , PM 10 and NO 2 and in terms of stroke subtypes, positive associations between ischaemic stroke and 'overall' exposure (typically the shortest lag available) to PM 2.5 and NO 2 and between haemorrhagic stroke and 'overall' exposure to NO 2 . 1 However, our study was relatively small, with our analysis based on 1758 strokes of which 1311 were ischaemic and 256 haemorrhagic.…”

“…Although few studies are able to consider these disease categories separately, a small study in Mantua, Italy, 27 found evidence of a positive association between PM 10 exposure and same day hospital admission for TACI in men only and for LACI in men and women. When we investigated these subtypes in our analysis (table 5), we found small non-significant, though positive, associations between TACI (number of cases=187) and both PM 2.5 and PM 10 , with the percentage increase in risk per 10 mg/m 3 increase in pollutant estimated at 5.9% (95% CI −15.9% to 33.3%) for PM 2.5 and 2.5% (95% CI −12.4% to 19.9%) for PM 10 . However, the CIs were again particularly wide.…”

“…For PM 10 and haemorrhagic stroke, the association was both negative and statistically significant with an estimated reduction in risk of 14.6% (95% CI 0.7% to 26.5%) per 10 mg/m 3 increase in pollutant. This negative association persisted following adjustment for O 3 .…”

“…7 In particular, we would point to the method of case definition which involves the identification of cases from various sources by registry doctors and nurses and the collection of sufficiently detailed information to facilitate the classification of cases into various stroke subtypes. 10 In terms of exposure information, one advantage of using modelled rather than monitored pollution data is that we can obtain temporally resolved daily pollutant outputs at fine spatial resolution such as postcode of residence with limited missing data. However, both monitored and modelled pollution are likely to be subject to measurement error.…”